Significance Testing: We Can Do Better.

This paper advocates abandoning null hypothesis statistical tests (NHST) in favour of reporting confidence intervals. The case against NHST, which has been made repeatedly in multiple disciplines and is growing in awareness and acceptance, is introduced and discussed. Accounting as an empirical rese...

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Publicado en:Abacus Vol. 52; no. 2; pp. 319 - 343
Autor principal: Dyckman, Thomas R.
Formato: Artículo
Publicado: Wiley-Blackwell Jun2016
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        116102063
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        atl: Significance Testing: We Can Do Better.
      aug:
        au: Dyckman, Thomas R.
        affil: Cornell University and Adjunct Professor at Florida Gulf Coast University
      su:
        Statistical hypothesis testing
        Null hypothesis
        Confidence intervals
        Empirical research
        Test interpretation
      sug:
        subj:
          Statistical hypothesis testing
          Null hypothesis
          Confidence intervals
          Empirical research
          Test interpretation
      keyword:
        Bayesian
        Confidence interval reporting
        Frequentist
        Meta‐analysis
        Meta-analysis
      ab: This paper advocates abandoning null hypothesis statistical tests (NHST) in favour of reporting confidence intervals. The case against NHST, which has been made repeatedly in multiple disciplines and is growing in awareness and acceptance, is introduced and discussed. Accounting as an empirical research discipline appears to be the last of the research communities to face up to the inherent problems of significance test use and abuse. The paper encourages adoption of a meta-analysis approach which allows for the inclusion of replication studies in the assessment of evidence. This approach requires abandoning the typical NHST process and its reliance on p-values. However, given that NHST has deep roots and wide 'social acceptance' in the empirical testing community, modifications to NHST are suggested so as to partly counter the weakness of this statistical testing method.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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